Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images
The identification and detection of planting holes, combined with UAV technology, provides an effective solution to the challenges posed by manual counting, high labor costs, and low efficiency in large-scale planting operations. However, existing target detection algorithms face difficulties in ide...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2614 |
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| author | Kaiyuan Long Shibo Li Jiangping Long Hui Lin Yang Yin |
| author_facet | Kaiyuan Long Shibo Li Jiangping Long Hui Lin Yang Yin |
| author_sort | Kaiyuan Long |
| collection | DOAJ |
| description | The identification and detection of planting holes, combined with UAV technology, provides an effective solution to the challenges posed by manual counting, high labor costs, and low efficiency in large-scale planting operations. However, existing target detection algorithms face difficulties in identifying planting holes based on their edge features, particularly in complex environments. To address this issue, a target detection network named YOLO-PH was designed to efficiently and rapidly detect planting holes in complex environments. Compared to the YOLOv8 network, the proposed YOLO-PH network incorporates the C2f_DyGhostConv module as a replacement for the original C2f module in both the backbone network and neck network. Furthermore, the ATSS label allocation method is employed to optimize sample allocation and enhance detection effectiveness. Lastly, our proposed Siblings Detection Head reduces computational burden while significantly improving detection performance. Ablation experiments demonstrate that compared to baseline models, YOLO-PH exhibits notable improvements of 1.3% in mAP50 and 1.1% in mAP50:95 while simultaneously achieving a reduction of 48.8% in FLOPs and an impressive increase of 26.8 FPS (frames per second) in detection speed. In practical applications for detecting indistinct boundary planting holes within complex scenarios, our algorithm consistently outperforms other detection networks with exceptional precision (F1-score = 0.95), low computational cost, rapid detection speed, and robustness, thus laying a solid foundation for advancing precision agriculture. |
| format | Article |
| id | doaj-art-a4214a6163334ea38276d4e77297cec8 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a4214a6163334ea38276d4e77297cec82025-08-20T03:02:58ZengMDPI AGRemote Sensing2072-42922025-07-011715261410.3390/rs17152614Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV ImagesKaiyuan Long0Shibo Li1Jiangping Long2Hui Lin3Yang Yin4Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaGuizhou Forestry Reconnaissance & Design Co., Ltd., Guiyang 550001, ChinaResearch Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaThe identification and detection of planting holes, combined with UAV technology, provides an effective solution to the challenges posed by manual counting, high labor costs, and low efficiency in large-scale planting operations. However, existing target detection algorithms face difficulties in identifying planting holes based on their edge features, particularly in complex environments. To address this issue, a target detection network named YOLO-PH was designed to efficiently and rapidly detect planting holes in complex environments. Compared to the YOLOv8 network, the proposed YOLO-PH network incorporates the C2f_DyGhostConv module as a replacement for the original C2f module in both the backbone network and neck network. Furthermore, the ATSS label allocation method is employed to optimize sample allocation and enhance detection effectiveness. Lastly, our proposed Siblings Detection Head reduces computational burden while significantly improving detection performance. Ablation experiments demonstrate that compared to baseline models, YOLO-PH exhibits notable improvements of 1.3% in mAP50 and 1.1% in mAP50:95 while simultaneously achieving a reduction of 48.8% in FLOPs and an impressive increase of 26.8 FPS (frames per second) in detection speed. In practical applications for detecting indistinct boundary planting holes within complex scenarios, our algorithm consistently outperforms other detection networks with exceptional precision (F1-score = 0.95), low computational cost, rapid detection speed, and robustness, thus laying a solid foundation for advancing precision agriculture.https://www.mdpi.com/2072-4292/17/15/2614planting holescomplex environmentYOLO-PHobject detectionUAV |
| spellingShingle | Kaiyuan Long Shibo Li Jiangping Long Hui Lin Yang Yin Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images Remote Sensing planting holes complex environment YOLO-PH object detection UAV |
| title | Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images |
| title_full | Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images |
| title_fullStr | Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images |
| title_full_unstemmed | Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images |
| title_short | Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images |
| title_sort | detecting planting holes using improved yolo ph algorithm with uav images |
| topic | planting holes complex environment YOLO-PH object detection UAV |
| url | https://www.mdpi.com/2072-4292/17/15/2614 |
| work_keys_str_mv | AT kaiyuanlong detectingplantingholesusingimprovedyolophalgorithmwithuavimages AT shiboli detectingplantingholesusingimprovedyolophalgorithmwithuavimages AT jiangpinglong detectingplantingholesusingimprovedyolophalgorithmwithuavimages AT huilin detectingplantingholesusingimprovedyolophalgorithmwithuavimages AT yangyin detectingplantingholesusingimprovedyolophalgorithmwithuavimages |